
"""simple docstring"""



import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
 raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
 raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
a       :						Union[str, Any]       		=		logging.get_logger(__name__)

a       :						Any       		=		'''The Nymphenburg Palace is a beautiful palace in Munich!'''


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = {
        """attention_cell""": """multi_head""",
        """num_layers""": 4,
        """units""": 1_0_2_4,
        """hidden_size""": 7_6_8,
        """max_length""": 5_1_2,
        """num_heads""": 8,
        """scaled""": True,
        """dropout""": 0.1,
        """use_residual""": True,
        """embed_size""": 1_0_2_4,
        """embed_dropout""": 0.1,
        """word_embed""": None,
        """layer_norm_eps""": 1e-5,
        """token_type_vocab_size""": 2,
    }

    _UpperCAmelCase        = bort_4_8_768_1024_hparams

    # Let's construct the original Bort model here
    # Taken from official BERT implementation, see:
    # https://github.com/alexa/bort/blob/master/bort/bort.py
    _UpperCAmelCase        = BERTEncoder(
        attention_cell=predefined_args["""attention_cell"""]       ,   num_layers=predefined_args["""num_layers"""]       ,   units=predefined_args["""units"""]       ,   hidden_size=predefined_args["""hidden_size"""]       ,   max_length=predefined_args["""max_length"""]       ,   num_heads=predefined_args["""num_heads"""]       ,   scaled=predefined_args["""scaled"""]       ,   dropout=predefined_args["""dropout"""]       ,   output_attention=_A       ,   output_all_encodings=_A       ,   use_residual=predefined_args["""use_residual"""]       ,   activation=predefined_args.get("""activation"""       ,   """gelu"""   )       ,   layer_norm_eps=predefined_args.get("""layer_norm_eps"""       ,   _A   )       ,   )

    # Vocab information needs to be fetched first
    # It's the same as RoBERTa, so RobertaTokenizer can be used later
    _UpperCAmelCase        = """openwebtext_ccnews_stories_books_cased"""

    # Specify download folder to Gluonnlp's vocab
    _UpperCAmelCase        = os.path.join(get_home_dir()       ,   """models"""   )
    _UpperCAmelCase        = _load_vocab(_A       ,   _A       ,   _A       ,   cls=_A   )

    _UpperCAmelCase        = nlp.model.BERTModel(
        _A       ,   len(_A   )       ,   units=predefined_args["""units"""]       ,   embed_size=predefined_args["""embed_size"""]       ,   embed_dropout=predefined_args["""embed_dropout"""]       ,   word_embed=predefined_args["""word_embed"""]       ,   use_pooler=_A       ,   use_token_type_embed=_A       ,   token_type_vocab_size=predefined_args["""token_type_vocab_size"""]       ,   use_classifier=_A       ,   use_decoder=_A       ,   )

    original_bort.load_parameters(_A       ,   cast_dtype=_A       ,   ignore_extra=_A   )
    _UpperCAmelCase        = original_bort._collect_params_with_prefix()

    # Build our config 🤗
    _UpperCAmelCase        = {
        """architectures""": ["""BertForMaskedLM"""],
        """attention_probs_dropout_prob""": predefined_args["""dropout"""],
        """hidden_act""": """gelu""",
        """hidden_dropout_prob""": predefined_args["""dropout"""],
        """hidden_size""": predefined_args["""embed_size"""],
        """initializer_range""": 0.02,
        """intermediate_size""": predefined_args["""hidden_size"""],
        """layer_norm_eps""": predefined_args["""layer_norm_eps"""],
        """max_position_embeddings""": predefined_args["""max_length"""],
        """model_type""": """bort""",
        """num_attention_heads""": predefined_args["""num_heads"""],
        """num_hidden_layers""": predefined_args["""num_layers"""],
        """pad_token_id""": 1,  # 2 = BERT, 1 = RoBERTa
        """type_vocab_size""": 1,  # 2 = BERT, 1 = RoBERTa
        """vocab_size""": len(_A   ),
    }

    _UpperCAmelCase        = BertConfig.from_dict(_A   )
    _UpperCAmelCase        = BertForMaskedLM(_A   )
    hf_bort_model.eval()

    # Parameter mapping table (Gluonnlp to Transformers)
    # * denotes layer index
    #
    # | Gluon Parameter                                                | Transformers Parameter
    # | -------------------------------------------------------------- | ----------------------
    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

    # Helper function to convert MXNET Arrays to PyTorch
    def to_torch(_A   ) -> nn.Parameter:
        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()   )   )

    # Check param shapes and map new HF param back
    def check_and_map_params(_A       ,   _A   ):
        _UpperCAmelCase        = hf_param.shape

        _UpperCAmelCase        = to_torch(params[gluon_param]   )
        _UpperCAmelCase        = gluon_param.shape

        assert (
            shape_hf == shape_gluon
        ), F"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

        return gluon_param

    _UpperCAmelCase        = check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight       ,   """word_embed.0.weight"""   )
    _UpperCAmelCase        = check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight       ,   """encoder.position_weight"""   )
    _UpperCAmelCase        = check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias       ,   """encoder.layer_norm.beta"""   )
    _UpperCAmelCase        = check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.weight       ,   """encoder.layer_norm.gamma"""   )

    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
    _UpperCAmelCase        = torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

    for i in range(hf_bort_config.num_hidden_layers   ):
        _UpperCAmelCase        = hf_bort_model.bert.encoder.layer[i]

        # self attention
        _UpperCAmelCase        = layer.attention.self

        _UpperCAmelCase        = check_and_map_params(
            self_attn.key.bias.data       ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""   )

        _UpperCAmelCase        = check_and_map_params(
            self_attn.key.weight.data       ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""   )
        _UpperCAmelCase        = check_and_map_params(
            self_attn.query.bias.data       ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""   )
        _UpperCAmelCase        = check_and_map_params(
            self_attn.query.weight.data       ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""   )
        _UpperCAmelCase        = check_and_map_params(
            self_attn.value.bias.data       ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""   )
        _UpperCAmelCase        = check_and_map_params(
            self_attn.value.weight.data       ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""   )

        # self attention output
        _UpperCAmelCase        = layer.attention.output

        _UpperCAmelCase        = check_and_map_params(
            self_output.dense.bias       ,   F"""encoder.transformer_cells.{i}.proj.bias"""   )
        _UpperCAmelCase        = check_and_map_params(
            self_output.dense.weight       ,   F"""encoder.transformer_cells.{i}.proj.weight"""   )
        _UpperCAmelCase        = check_and_map_params(
            self_output.LayerNorm.bias       ,   F"""encoder.transformer_cells.{i}.layer_norm.beta"""   )
        _UpperCAmelCase        = check_and_map_params(
            self_output.LayerNorm.weight       ,   F"""encoder.transformer_cells.{i}.layer_norm.gamma"""   )

        # intermediate
        _UpperCAmelCase        = layer.intermediate

        _UpperCAmelCase        = check_and_map_params(
            intermediate.dense.bias       ,   F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""   )
        _UpperCAmelCase        = check_and_map_params(
            intermediate.dense.weight       ,   F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""   )

        # output
        _UpperCAmelCase        = layer.output

        _UpperCAmelCase        = check_and_map_params(
            bert_output.dense.bias       ,   F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""   )
        _UpperCAmelCase        = check_and_map_params(
            bert_output.dense.weight       ,   F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""   )
        _UpperCAmelCase        = check_and_map_params(
            bert_output.LayerNorm.bias       ,   F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""   )
        _UpperCAmelCase        = check_and_map_params(
            bert_output.LayerNorm.weight       ,   F"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""   )

    # Save space and energy 🎄
    hf_bort_model.half()

    # Compare output of both models
    _UpperCAmelCase        = RobertaTokenizer.from_pretrained("""roberta-base"""   )

    _UpperCAmelCase        = tokenizer.encode_plus(_A   )["""input_ids"""]

    # Get gluon output
    _UpperCAmelCase        = mx.nd.array([input_ids]   )
    _UpperCAmelCase        = original_bort(inputs=_A       ,   token_types=[]   )

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(_A   )
    _UpperCAmelCase        = BertModel.from_pretrained(_A   )
    hf_bort_model.eval()

    _UpperCAmelCase        = tokenizer.encode_plus(_A       ,   return_tensors="""pt"""   )
    _UpperCAmelCase        = hf_bort_model(**_A   )[0]

    _UpperCAmelCase        = output_gluon[0].asnumpy()
    _UpperCAmelCase        = output_hf[0].detach().numpy()

    _UpperCAmelCase        = np.max(np.abs(hf_layer - gluon_layer   )   ).item()
    _UpperCAmelCase        = np.allclose(_A       ,   _A       ,   atol=1e-3   )

    if success:
        print("""✔️ Both model do output the same tensors"""   )
    else:
        print("""❌ Both model do **NOT** output the same tensors"""   )
        print("""Absolute difference is:"""       ,   _A   )


if __name__ == "__main__":
 a       :						List[Any]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						int       		=		parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""



import os
from math import logaa


def 			_UpperCamelCase						(    _A = "base_exp.txt"   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = 0
    _UpperCAmelCase        = 0
    for i, line in enumerate(open(os.path.join(os.path.dirname(_A   )       ,   _A   )   )   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = list(map(_A       ,   line.split(""","""   )   )   )
        if x * logaa(_A   ) > largest:
            _UpperCAmelCase        = x * logaa(_A   )
            _UpperCAmelCase        = i + 1
    return result


if __name__ == "__main__":
 print(solution())